Overview

Dataset statistics

Number of variables18
Number of observations20758
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 MiB
Average record size in memory650.9 B

Variable types

Numeric9
Categorical5
Boolean4

Alerts

Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
NObeyesdad is highly overall correlated with Gender and 1 other fieldsHigh correlation
Weight is highly overall correlated with family_history_with_overweightHigh correlation
family_history_with_overweight is highly overall correlated with NObeyesdad and 1 other fieldsHigh correlation
FAVC is highly imbalanced (57.9%)Imbalance
CAEC is highly imbalanced (61.0%)Imbalance
SMOKE is highly imbalanced (90.7%)Imbalance
SCC is highly imbalanced (79.0%)Imbalance
MTRANS is highly imbalanced (63.7%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
FAF has 5044 (24.3%) zerosZeros
TUE has 6566 (31.6%) zerosZeros

Reproduction

Analysis started2024-06-01 03:24:11.277560
Analysis finished2024-06-01 03:24:44.055477
Duration32.78 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct20758
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10378.5
Minimum0
Maximum20757
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:44.300217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1037.85
Q15189.25
median10378.5
Q315567.75
95-th percentile19719.15
Maximum20757
Range20757
Interquartile range (IQR)10378.5

Descriptive statistics

Standard deviation5992.4628
Coefficient of variation (CV)0.57739199
Kurtosis-1.2
Mean10378.5
Median Absolute Deviation (MAD)5189.5
Skewness0
Sum2.154369 × 108
Variance35909610
MonotonicityStrictly increasing
2024-05-31T23:24:44.489049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
13826 1
 
< 0.1%
13844 1
 
< 0.1%
13843 1
 
< 0.1%
13842 1
 
< 0.1%
13841 1
 
< 0.1%
13840 1
 
< 0.1%
13839 1
 
< 0.1%
13838 1
 
< 0.1%
13837 1
 
< 0.1%
Other values (20748) 20748
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
20757 1
< 0.1%
20756 1
< 0.1%
20755 1
< 0.1%
20754 1
< 0.1%
20753 1
< 0.1%
20752 1
< 0.1%
20751 1
< 0.1%
20750 1
< 0.1%
20749 1
< 0.1%
20748 1
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Female
10422 
Male
10336 

Length

Max length6
Median length6
Mean length5.004143
Min length4

Characters and Unicode

Total characters103876
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 10422
50.2%
Male 10336
49.8%

Length

2024-05-31T23:24:44.677167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-31T23:24:44.842140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 10422
50.2%
male 10336
49.8%

Most occurring characters

ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Age
Real number (ℝ)

Distinct1703
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.841804
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:45.011012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.989577
Q120
median22.815416
Q326
95-th percentile35.460417
Maximum61
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.688072
Coefficient of variation (CV)0.23857556
Kurtosis3.7005977
Mean23.841804
Median Absolute Deviation (MAD)3.184584
Skewness1.5862517
Sum494908.18
Variance32.354163
MonotonicityNot monotonic
2024-05-31T23:24:45.197145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1916
 
9.2%
26 1806
 
8.7%
21 1643
 
7.9%
23 1202
 
5.8%
19 886
 
4.3%
20 530
 
2.6%
17 516
 
2.5%
22 512
 
2.5%
33 209
 
1.0%
24 164
 
0.8%
Other values (1693) 11374
54.8%
ValueCountFrequency (%)
14 5
 
< 0.1%
15 3
 
< 0.1%
16 109
0.5%
16.093234 4
 
< 0.1%
16.120699 1
 
< 0.1%
16.129279 10
 
< 0.1%
16.140751 1
 
< 0.1%
16.172992 3
 
< 0.1%
16.178483 1
 
< 0.1%
16.198153 2
 
< 0.1%
ValueCountFrequency (%)
61 2
 
< 0.1%
56 1
 
< 0.1%
55.493687 1
 
< 0.1%
55.272573 1
 
< 0.1%
55.24625 2
 
< 0.1%
55.137881 5
 
< 0.1%
55.022494 13
 
0.1%
55 38
0.2%
53.783977 2
 
< 0.1%
52 2
 
< 0.1%

Height
Real number (ℝ)

HIGH CORRELATION 

Distinct1833
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7002449
Minimum1.45
Maximum1.975663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:45.382810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.559005
Q11.631856
median1.7
Q31.762887
95-th percentile1.84629
Maximum1.975663
Range0.525663
Interquartile range (IQR)0.131031

Descriptive statistics

Standard deviation0.087311906
Coefficient of variation (CV)0.051352546
Kurtosis-0.55963409
Mean1.7002449
Median Absolute Deviation (MAD)0.066055
Skewness0.015802676
Sum35293.684
Variance0.0076233689
MonotonicityNot monotonic
2024-05-31T23:24:45.581694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 1334
 
6.4%
1.65 782
 
3.8%
1.6 672
 
3.2%
1.75 659
 
3.2%
1.8 517
 
2.5%
1.62 398
 
1.9%
1.72 317
 
1.5%
1.56 256
 
1.2%
1.63 239
 
1.2%
1.55 211
 
1.0%
Other values (1823) 15373
74.1%
ValueCountFrequency (%)
1.45 2
 
< 0.1%
1.456346 2
 
< 0.1%
1.463167 1
 
< 0.1%
1.48 9
 
< 0.1%
1.481682 1
 
< 0.1%
1.483284 2
 
< 0.1%
1.486484 3
 
< 0.1%
1.489409 2
 
< 0.1%
1.498561 3
 
< 0.1%
1.5 165
0.8%
ValueCountFrequency (%)
1.975663 4
 
< 0.1%
1.947406 4
 
< 0.1%
1.942725 4
 
< 0.1%
1.931263 2
 
< 0.1%
1.931242 1
 
< 0.1%
1.930416 1
 
< 0.1%
1.93 12
0.1%
1.92 2
 
< 0.1%
1.919557 2
 
< 0.1%
1.919543 5
< 0.1%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct1979
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.887768
Minimum39
Maximum165.05727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:45.766729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile49
Q166
median84.064875
Q3111.60055
95-th percentile132.11649
Maximum165.05727
Range126.05727
Interquartile range (IQR)45.600553

Descriptive statistics

Standard deviation26.379443
Coefficient of variation (CV)0.3001492
Kurtosis-0.99704333
Mean87.887768
Median Absolute Deviation (MAD)22.947131
Skewness0.09318728
Sum1824374.3
Variance695.87502
MonotonicityNot monotonic
2024-05-31T23:24:45.968179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 866
 
4.2%
75 630
 
3.0%
50 618
 
3.0%
60 506
 
2.4%
70 486
 
2.3%
45 323
 
1.6%
65 306
 
1.5%
85 297
 
1.4%
78 293
 
1.4%
42 275
 
1.3%
Other values (1969) 16158
77.8%
ValueCountFrequency (%)
39 1
 
< 0.1%
39.101805 5
< 0.1%
39.12631 1
 
< 0.1%
39.371523 2
 
< 0.1%
39.535047 1
 
< 0.1%
39.581159 1
 
< 0.1%
39.591159 1
 
< 0.1%
39.695295 3
< 0.1%
39.850137 3
< 0.1%
40 4
< 0.1%
ValueCountFrequency (%)
165.057269 4
 
< 0.1%
160.935351 14
0.1%
160.639405 3
 
< 0.1%
155.872093 3
 
< 0.1%
155.242672 3
 
< 0.1%
154.618446 4
 
< 0.1%
153.959945 3
 
< 0.1%
153.149491 9
< 0.1%
152.720545 5
 
< 0.1%
152.567671 6
< 0.1%

family_history_with_overweight
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
True
17014 
False
3744 
ValueCountFrequency (%)
True 17014
82.0%
False 3744
 
18.0%
2024-05-31T23:24:46.127181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FAVC
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
True
18982 
False
 
1776
ValueCountFrequency (%)
True 18982
91.4%
False 1776
 
8.6%
2024-05-31T23:24:46.262772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FCVC
Real number (ℝ)

Distinct934
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4459084
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:46.425746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.826885
Q12
median2.393837
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53321815
Coefficient of variation (CV)0.21800414
Kurtosis-0.89299613
Mean2.4459084
Median Absolute Deviation (MAD)0.408223
Skewness-0.35661125
Sum50772.166
Variance0.2843216
MonotonicityNot monotonic
2024-05-31T23:24:46.647045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 7658
36.9%
2 7653
36.9%
1 275
 
1.3%
2.9673 121
 
0.6%
2.766612 54
 
0.3%
2.938616 46
 
0.2%
2.9553 39
 
0.2%
2.57649 30
 
0.1%
2.819934 30
 
0.1%
2.225149 29
 
0.1%
Other values (924) 4823
23.2%
ValueCountFrequency (%)
1 275
1.3%
1.002564 1
 
< 0.1%
1.003566 6
 
< 0.1%
1.005578 13
 
0.1%
1.006436 1
 
< 0.1%
1.00876 5
 
< 0.1%
1.021136 1
 
< 0.1%
1.031149 18
 
0.1%
1.036159 7
 
< 0.1%
1.036414 6
 
< 0.1%
ValueCountFrequency (%)
3 7658
36.9%
2.998441 2
 
< 0.1%
2.997951 11
 
0.1%
2.997524 5
 
< 0.1%
2.997062 1
 
< 0.1%
2.996717 11
 
0.1%
2.996186 6
 
< 0.1%
2.995599 3
 
< 0.1%
2.99448 4
 
< 0.1%
2.993634 1
 
< 0.1%

NCP
Real number (ℝ)

Distinct689
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7613323
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:46.858267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile3.520555
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7053746
Coefficient of variation (CV)0.2554472
Kurtosis1.8372696
Mean2.7613323
Median Absolute Deviation (MAD)0
Skewness-1.5622533
Sum57319.736
Variance0.49755332
MonotonicityNot monotonic
2024-05-31T23:24:47.051527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 14706
70.8%
1 1976
 
9.5%
4 715
 
3.4%
2.993623 39
 
0.2%
2.695396 25
 
0.1%
2.977909 22
 
0.1%
2.992083 21
 
0.1%
1.894384 21
 
0.1%
2.658837 20
 
0.1%
2.938902 20
 
0.1%
Other values (679) 3193
 
15.4%
ValueCountFrequency (%)
1 1976
9.5%
1.000283 5
 
< 0.1%
1.000414 2
 
< 0.1%
1.00061 7
 
< 0.1%
1.001383 6
 
< 0.1%
1.001542 8
 
< 0.1%
1.001633 8
 
< 0.1%
1.009426 4
 
< 0.1%
1.010319 6
 
< 0.1%
1.014916 4
 
< 0.1%
ValueCountFrequency (%)
4 715
3.4%
3.998766 3
 
< 0.1%
3.998618 6
 
< 0.1%
3.995957 5
 
< 0.1%
3.995147 5
 
< 0.1%
3.994588 4
 
< 0.1%
3.990925 2
 
< 0.1%
3.98955 5
 
< 0.1%
3.989492 1
 
< 0.1%
3.987707 7
 
< 0.1%

CAEC
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Sometimes
17529 
Frequently
2472 
Always
 
478
no
 
279

Length

Max length10
Median length9
Mean length8.9559206
Min length2

Characters and Unicode

Total characters185907
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowFrequently
3rd rowSometimes
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 17529
84.4%
Frequently 2472
 
11.9%
Always 478
 
2.3%
no 279
 
1.3%

Length

2024-05-31T23:24:47.243938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-31T23:24:47.409438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 17529
84.4%
frequently 2472
 
11.9%
always 478
 
2.3%
no 279
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 185907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 185907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 185907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

SMOKE
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
False
20513 
True
 
245
ValueCountFrequency (%)
False 20513
98.8%
True 245
 
1.2%
2024-05-31T23:24:47.552463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

CH2O
Real number (ℝ)

Distinct1506
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0294182
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:47.721844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.792022
median2
Q32.549617
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.757595

Descriptive statistics

Standard deviation0.60846702
Coefficient of variation (CV)0.29982337
Kurtosis-0.74417999
Mean2.0294182
Median Absolute Deviation (MAD)0.409582
Skewness-0.21250583
Sum42126.664
Variance0.37023211
MonotonicityNot monotonic
2024-05-31T23:24:47.943718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6616
31.9%
1 2799
 
13.5%
3 1571
 
7.6%
2.825629 77
 
0.4%
2.868167 60
 
0.3%
2.619517 57
 
0.3%
2.625537 56
 
0.3%
2.72005 52
 
0.3%
2.770732 51
 
0.2%
2.613928 47
 
0.2%
Other values (1496) 9372
45.1%
ValueCountFrequency (%)
1 2799
13.5%
1.000463 5
 
< 0.1%
1.000536 4
 
< 0.1%
1.000544 7
 
< 0.1%
1.000695 3
 
< 0.1%
1.001208 1
 
< 0.1%
1.001995 2
 
< 0.1%
1.002292 8
 
< 0.1%
1.003063 3
 
< 0.1%
1.003531 1
 
< 0.1%
ValueCountFrequency (%)
3 1571
7.6%
2.999495 3
 
< 0.1%
2.99675 1
 
< 0.1%
2.994515 2
 
< 0.1%
2.993448 1
 
< 0.1%
2.991671 2
 
< 0.1%
2.989389 5
 
< 0.1%
2.988771 5
 
< 0.1%
2.987718 7
 
< 0.1%
2.987406 2
 
< 0.1%

SCC
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
False
20071 
True
 
687
ValueCountFrequency (%)
False 20071
96.7%
True 687
 
3.3%
2024-05-31T23:24:48.117821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FAF
Real number (ℝ)

ZEROS 

Distinct1360
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98174656
Minimum0
Maximum3
Zeros5044
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:48.589254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.008013
median1
Q31.587406
95-th percentile2.545707
Maximum3
Range3
Interquartile range (IQR)1.579393

Descriptive statistics

Standard deviation0.83830198
Coefficient of variation (CV)0.85388838
Kurtosis-0.49484242
Mean0.98174656
Median Absolute Deviation (MAD)0.870098
Skewness0.50572622
Sum20379.095
Variance0.7027502
MonotonicityNot monotonic
2024-05-31T23:24:48.775718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5044
24.3%
1 4097
19.7%
2 2391
 
11.5%
3 800
 
3.9%
1.097905 56
 
0.3%
1.427037 47
 
0.2%
0.01586 47
 
0.2%
1.68249 39
 
0.2%
1.999836 36
 
0.2%
1.465931 32
 
0.2%
Other values (1350) 8169
39.4%
ValueCountFrequency (%)
0 5044
24.3%
9.6 × 10-510
 
< 0.1%
0.000272 9
 
< 0.1%
0.000454 11
 
0.1%
0.001015 12
 
0.1%
0.001086 7
 
< 0.1%
0.001272 5
 
< 0.1%
0.001297 25
 
0.1%
0.00203 6
 
< 0.1%
0.00342 10
 
< 0.1%
ValueCountFrequency (%)
3 800
3.9%
2.999918 3
 
< 0.1%
2.993666 1
 
< 0.1%
2.977543 1
 
< 0.1%
2.971832 3
 
< 0.1%
2.939733 1
 
< 0.1%
2.936551 4
 
< 0.1%
2.931527 3
 
< 0.1%
2.910633 1
 
< 0.1%
2.892922 24
 
0.1%

TUE
Real number (ℝ)

ZEROS 

Distinct1297
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61675622
Minimum0
Maximum2
Zeros6566
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-05-31T23:24:48.980704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.573887
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60211348
Coefficient of variation (CV)0.97625845
Kurtosis-0.4177298
Mean0.61675622
Median Absolute Deviation (MAD)0.450026
Skewness0.67041134
Sum12802.626
Variance0.36254064
MonotonicityNot monotonic
2024-05-31T23:24:49.184959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6566
31.6%
1 4326
20.8%
2 1133
 
5.5%
0.0026 78
 
0.4%
0.723154 65
 
0.3%
0.088236 53
 
0.3%
0.15171 52
 
0.3%
0.630866 45
 
0.2%
0.62535 41
 
0.2%
0.200379 38
 
0.2%
Other values (1287) 8361
40.3%
ValueCountFrequency (%)
0 6566
31.6%
7.3 × 10-52
 
< 0.1%
0.000355 2
 
< 0.1%
0.000436 3
 
< 0.1%
0.001096 5
 
< 0.1%
0.00133 15
 
0.1%
0.001337 8
 
< 0.1%
0.00135 1
 
< 0.1%
0.001518 11
 
0.1%
0.00159 11
 
0.1%
ValueCountFrequency (%)
2 1133
5.5%
1.99219 2
 
< 0.1%
1.990925 1
 
< 0.1%
1.990617 4
 
< 0.1%
1.983678 1
 
< 0.1%
1.980875 6
 
< 0.1%
1.978043 9
 
< 0.1%
1.972926 6
 
< 0.1%
1.97117 12
 
0.1%
1.969507 6
 
< 0.1%

CALC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Sometimes
15066 
no
5163 
Frequently
 
529

Length

Max length10
Median length9
Mean length7.2844205
Min length2

Characters and Unicode

Total characters151210
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowno
3rd rowno
4th rowSometimes
5th rowSometimes

Common Values

ValueCountFrequency (%)
Sometimes 15066
72.6%
no 5163
 
24.9%
Frequently 529
 
2.5%

Length

2024-05-31T23:24:49.397439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-31T23:24:49.551110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sometimes 15066
72.6%
no 5163
 
24.9%
frequently 529
 
2.5%

Most occurring characters

ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

MTRANS
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Public_Transportation
16687 
Automobile
3534 
Walking
 
467
Motorbike
 
38
Bike
 
32

Length

Max length21
Median length21
Mean length18.764139
Min length4

Characters and Unicode

Total characters389506
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowAutomobile
3rd rowPublic_Transportation
4th rowPublic_Transportation
5th rowPublic_Transportation

Common Values

ValueCountFrequency (%)
Public_Transportation 16687
80.4%
Automobile 3534
 
17.0%
Walking 467
 
2.2%
Motorbike 38
 
0.2%
Bike 32
 
0.2%

Length

2024-05-31T23:24:49.721303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-31T23:24:49.875834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
public_transportation 16687
80.4%
automobile 3534
 
17.0%
walking 467
 
2.2%
motorbike 38
 
0.2%
bike 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 40518
10.4%
i 37445
 
9.6%
t 36946
 
9.5%
a 33841
 
8.7%
n 33841
 
8.7%
r 33412
 
8.6%
l 20688
 
5.3%
b 20259
 
5.2%
u 20221
 
5.2%
P 16687
 
4.3%
Other values (13) 95648
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 389506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 40518
10.4%
i 37445
 
9.6%
t 36946
 
9.5%
a 33841
 
8.7%
n 33841
 
8.7%
r 33412
 
8.6%
l 20688
 
5.3%
b 20259
 
5.2%
u 20221
 
5.2%
P 16687
 
4.3%
Other values (13) 95648
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 389506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 40518
10.4%
i 37445
 
9.6%
t 36946
 
9.5%
a 33841
 
8.7%
n 33841
 
8.7%
r 33412
 
8.6%
l 20688
 
5.3%
b 20259
 
5.2%
u 20221
 
5.2%
P 16687
 
4.3%
Other values (13) 95648
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 389506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 40518
10.4%
i 37445
 
9.6%
t 36946
 
9.5%
a 33841
 
8.7%
n 33841
 
8.7%
r 33412
 
8.6%
l 20688
 
5.3%
b 20259
 
5.2%
u 20221
 
5.2%
P 16687
 
4.3%
Other values (13) 95648
24.6%

NObeyesdad
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Obesity_Type_III
4046 
Obesity_Type_II
3248 
Normal_Weight
3082 
Obesity_Type_I
2910 
Insufficient_Weight
2523 
Other values (2)
4949 

Length

Max length19
Median length16
Mean length16.080692
Min length13

Characters and Unicode

Total characters333803
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverweight_Level_II
2nd rowNormal_Weight
3rd rowInsufficient_Weight
4th rowObesity_Type_III
5th rowOverweight_Level_II

Common Values

ValueCountFrequency (%)
Obesity_Type_III 4046
19.5%
Obesity_Type_II 3248
15.6%
Normal_Weight 3082
14.8%
Obesity_Type_I 2910
14.0%
Insufficient_Weight 2523
12.2%
Overweight_Level_II 2522
12.1%
Overweight_Level_I 2427
11.7%

Length

2024-05-31T23:24:50.061167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-31T23:24:50.239426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
obesity_type_iii 4046
19.5%
obesity_type_ii 3248
15.6%
normal_weight 3082
14.8%
obesity_type_i 2910
14.0%
insufficient_weight 2523
12.2%
overweight_level_ii 2522
12.1%
overweight_level_i 2427
11.7%

Most occurring characters

ValueCountFrequency (%)
e 48332
14.5%
_ 35911
 
10.8%
I 31538
 
9.4%
i 25804
 
7.7%
t 23281
 
7.0%
y 20408
 
6.1%
O 15153
 
4.5%
s 12727
 
3.8%
h 10554
 
3.2%
g 10554
 
3.2%
Other values (17) 99541
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 48332
14.5%
_ 35911
 
10.8%
I 31538
 
9.4%
i 25804
 
7.7%
t 23281
 
7.0%
y 20408
 
6.1%
O 15153
 
4.5%
s 12727
 
3.8%
h 10554
 
3.2%
g 10554
 
3.2%
Other values (17) 99541
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 48332
14.5%
_ 35911
 
10.8%
I 31538
 
9.4%
i 25804
 
7.7%
t 23281
 
7.0%
y 20408
 
6.1%
O 15153
 
4.5%
s 12727
 
3.8%
h 10554
 
3.2%
g 10554
 
3.2%
Other values (17) 99541
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 48332
14.5%
_ 35911
 
10.8%
I 31538
 
9.4%
i 25804
 
7.7%
t 23281
 
7.0%
y 20408
 
6.1%
O 15153
 
4.5%
s 12727
 
3.8%
h 10554
 
3.2%
g 10554
 
3.2%
Other values (17) 99541
29.8%

Interactions

2024-05-31T23:24:41.325264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:26.972054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:28.869220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:30.697706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:32.386064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:34.105450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:36.032148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:37.771523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:39.520272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:41.522326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:27.196525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:29.062126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:30.867867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:32.564809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:34.287191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:36.218661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:37.954037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:39.706776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:41.713751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:27.432292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:29.264205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:31.055264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:32.758042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:34.457542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:36.414786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:38.150658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:39.885030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:41.916110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:27.652053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:29.463618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:31.251724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:32.939479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:34.637144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:36.602297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:38.352744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:40.068450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:42.124642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:27.834359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:29.668527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:31.436190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:33.134171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:34.822044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:36.784309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:38.540679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:40.271879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:42.329949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:28.052147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:29.886313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:31.644641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:33.322269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:35.014996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:36.987360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:38.738803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:40.467280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:42.529353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:28.246233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:30.091409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:31.826355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:33.500718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:35.196140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:37.174789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:38.925006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:40.678685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:42.779849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:28.464421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:30.296753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:32.021068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:33.708477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:35.397547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:37.374034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:39.113465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:40.897752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:43.000350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:28.664860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:30.501310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:32.192658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:33.900316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:35.823225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:37.564167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:39.319881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-31T23:24:41.117581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-31T23:24:50.430187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPNObeyesdadSCCSMOKETUEWeightfamily_history_with_overweightid
Age1.0000.1570.1950.093-0.2780.1280.0970.2610.0160.364-0.1200.3500.1160.142-0.3030.4410.3010.010
CAEC0.1571.0000.0980.158-0.0860.136-0.0330.0710.0710.070-0.1420.3320.1280.021-0.0420.3700.3360.004
CALC0.1950.0981.000-0.0950.0840.117-0.1530.086-0.0870.074-0.1070.3100.0000.0210.059-0.2260.0150.006
CH2O0.0930.158-0.0951.0000.0580.1660.1070.3360.1880.1040.0830.3130.0790.049-0.0000.3490.2780.012
FAF-0.278-0.0860.0840.0581.0000.138-0.0950.3470.3180.1070.1110.2610.0780.039-0.011-0.0710.1880.017
FAVC0.1280.1360.1170.1660.1381.0000.0030.0200.1110.123-0.0100.2730.1110.0140.0520.2300.1520.004
FCVC0.097-0.033-0.1530.107-0.0950.0031.0000.404-0.1150.0990.1340.3250.0410.049-0.1300.2250.1340.005
Gender0.2610.0710.0860.3360.3470.0200.4041.0000.6370.168-0.0440.6190.0610.0630.0460.1490.095-0.001
Height0.0160.071-0.0870.1880.3180.111-0.1150.6371.0000.0850.1090.2680.1470.1100.0840.4200.3000.011
MTRANS0.3640.0700.0740.1040.1070.1230.0990.1680.0851.0000.0380.1670.0490.0430.208-0.0370.1330.014
NCP-0.120-0.142-0.1070.0830.111-0.0100.134-0.0440.1090.0381.0000.2200.0660.0100.128-0.0220.2230.001
NObeyesdad0.3500.3320.3100.3130.2610.2730.3250.6190.2680.1670.2201.0000.2220.101-0.0680.4290.5560.012
SCC0.1160.1280.0000.0790.0780.1110.0410.0610.1470.0490.0660.2221.0000.014-0.019-0.1860.166-0.011
SMOKE0.1420.0210.0210.0490.0390.0140.0490.0630.1100.0430.0100.1010.0141.000-0.0180.0410.0170.012
TUE-0.303-0.0420.059-0.000-0.0110.052-0.1300.0460.0840.2080.128-0.068-0.019-0.0181.000-0.0660.2060.008
Weight0.4410.370-0.2260.349-0.0710.2300.2250.1490.420-0.037-0.0220.429-0.1860.041-0.0661.0000.5850.014
family_history_with_overweight0.3010.3360.0150.2780.1880.1520.1340.0950.3000.1330.2230.5560.1660.0170.2060.5851.0000.010
id0.0100.0040.0060.0120.0170.0040.005-0.0010.0110.0140.0010.012-0.0110.0120.0080.0140.0101.000

Missing values

2024-05-31T23:24:43.255154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-31T23:24:43.647006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
00Male24.4430111.69999881.669950yesyes2.0000002.983297Sometimesno2.763573no0.0000000.976473SometimesPublic_TransportationOverweight_Level_II
11Female18.0000001.56000057.000000yesyes2.0000003.000000Frequentlyno2.000000no1.0000001.000000noAutomobileNormal_Weight
22Female18.0000001.71146050.165754yesyes1.8805341.411685Sometimesno1.910378no0.8660451.673584noPublic_TransportationInsufficient_Weight
33Female20.9527371.710730131.274851yesyes3.0000003.000000Sometimesno1.674061no1.4678630.780199SometimesPublic_TransportationObesity_Type_III
44Male31.6410811.91418693.798055yesyes2.6796641.971472Sometimesno1.979848no1.9679730.931721SometimesPublic_TransportationOverweight_Level_II
55Male18.1282491.74852451.552595yesyes2.9197513.000000Sometimesno2.137550no1.9300331.000000SometimesPublic_TransportationInsufficient_Weight
66Male29.8830211.754711112.725005yesyes1.9912403.000000Sometimesno2.000000no0.0000000.696948SometimesAutomobileObesity_Type_II
77Male29.8914731.750150118.206565yesyes1.3974683.000000Sometimesno2.000000no0.5986550.000000SometimesAutomobileObesity_Type_II
88Male17.0000001.70000070.000000noyes2.0000003.000000Sometimesno3.000000yes1.0000001.000000noPublic_TransportationOverweight_Level_I
99Female26.0000001.638836111.275646yesyes3.0000003.000000Sometimesno2.632253no0.0000000.218645SometimesPublic_TransportationObesity_Type_III
idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
2074820748Male21.0000001.75795889.048151yesyes1.8822353.000000Sometimesno2.000000no0.9886681.000000noPublic_TransportationOverweight_Level_II
2074920749Female25.7838651.646390104.835346yesyes3.0000003.000000Sometimesno1.530992no0.0158600.445495SometimesPublic_TransportationObesity_Type_III
2075020750Male18.8270081.75332180.000000yesyes2.8262512.256119Sometimesno2.137550no0.7537820.051858noPublic_TransportationOverweight_Level_I
2075120751Female21.0309091.605495133.466763yesyes3.0000003.000000Sometimesno2.839069no1.6834970.143675SometimesPublic_TransportationObesity_Type_III
2075220752Female40.0000001.55472877.561602yesyes2.0000003.000000Sometimesno1.131169no0.2817340.522259SometimesAutomobileObesity_Type_I
2075320753Male25.1370871.766626114.187096yesyes2.9195843.000000Sometimesno2.151809no1.3305190.196680SometimesPublic_TransportationObesity_Type_II
2075420754Male18.0000001.71000050.000000noyes3.0000004.000000Frequentlyno1.000000no2.0000001.000000SometimesPublic_TransportationInsufficient_Weight
2075520755Male20.1010261.819557105.580491yesyes2.4078173.000000Sometimesno2.000000no1.1580401.198439noPublic_TransportationObesity_Type_II
2075620756Male33.8529531.70000083.520113yesyes2.6712381.971472Sometimesno2.144838no0.0000000.973834noAutomobileOverweight_Level_II
2075720757Male26.6803761.816547118.134898yesyes3.0000003.000000Sometimesno2.003563no0.6844870.713823SometimesPublic_TransportationObesity_Type_II